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There's a magical and political storm brewing - and Aubrey Fitzwilliam is making sure he's right in the centre of it. Set in a world similar to Edwardian Britain, just before World War I, this fantasy series is full of magic, manners, conspiracy and intrigue, politics and personal trial - not to mention good old-fashioned fun. The Laws of Magic stars Aubrey Fitzwilliam, the son of a prominent ex-prime minister. He's brilliant at magic, but he's still stuck at school. At least he has his best friend, George, there to back him up. George would follow Aubrey anywhere - and with Aubrey's talent for thinking up impulsive and daring schemes that will get them both in trouble, that's no easy thing to do. From golems to top-secret submersibles, soul stealers to ghosts, The Laws of Magic is a cracking good read. Discover the magic in this free sampler that gives you chapters from each of the six books, so no matter where you're up to in the series, you can find out what's coming next for Aubrey and his friends.
An Introduction to Data Science with Python by Jeffrey S. Saltz and Jeffery M. Stanton provides readers who are new to Python and data science with a step-by-step walkthrough of the tools and techniques used to analyze data and generate predictive models. After introducing the basic concepts of data science, the book builds on these foundations to explain data science techniques using Python-based Jupyter Notebooks. The techniques include making tables and data frames, computing statistics, managing data, creating data visualizations, and building machine learning models. Each chapter breaks down the process into simple steps and components so students with no more than a high school algebra background will still find the concepts and code intelligible. Explanations are reinforced with linked practice questions throughout to check reader understanding. The book also covers advanced topics such as neural networks and deep learning, the basis of many recent and startling advances in machine learning and artificial intelligence. With their trademark humor and clear explanations, Saltz and Stanton provide a gentle introduction to this powerful data science tool. Included with this title: LMS Cartridge: Import this title’s instructor resources into your school’s learning management system (LMS) and save time. Don′t use an LMS? You can still access all of the same online resources for this title via the password-protected Instructor Resource Site.
This book presents the thoroughly refereed joint post-proceedings of three workshops held during the 17th International Conference on Conceptual Modeling, ER '98, in Singapore in November 1998. The 50 revised papers presented have gone through two rounds of reviewing and revision. The book is divided in sections on knowledge discovery, data mining, data and web warehousing, multidimensional databases, data warehouse design, caching, data dissemination, replication, mobile networks, mobile platforms, tracking and monitoring, collaborative work support, temporal data modelling, moving objects and spatial indexing, spatio-temporal databases, and video database contents.
The design of knowledge systems is finding myriad applications from corporate databases to general decision support in areas as diverse as engineering, manufacturing and other industrial processes, medicine, business, and economics. In engineering, for example, knowledge bases can be utilized for reliable electric power system operation. In medicine they support complex diagnoses, while in business they inform the process of strategic planning. Programmed securities trading and the defeat of chess champion Kasparov by IBM's Big Blue are two familiar examples of dedicated knowledge bases in combination with an expert system for decision-making.With volumes covering "Implementation," "Optimization," "Computer Techniques," and "Systems and Applications," this comprehensive set constitutes a unique reference source for students, practitioners, and researchers in computer science, engineering, and the broad range of applications areas for knowledge-based systems.